John Nielsen-Gammon is the Texas State Climatologist and a Professor of Atmospheric Sciences at Texas A&M University. Viewers may remember him from my snapshot of the Great Texas drought.

Reposted with permission:

It’s common knowledge among those who follow such things that global temperatures have not gone up very much in the past several years. This has caused many to believe that the recent lack of warming contradicts what climate models say should happen in response to the increasing Tyndall gases. This, in turn, has provoked the counterargument that the Earth is still warming, just on a longer time scale, or that the recent period is too short to yield statistically significant results.

These counterarguments are not compelling. Fundamentally, any change in global temperature, even if it’s just from one year to another, must have a cause. Saying that we need to look at longer time scales denies the need to find the cause of the actual global temperature changes (or lack thereof) at shorter time scales.

Such causes have been sought, and a few papers have proposed various combinations of cloud cover, volcanic aerosols, the El Niño/Southern Oscillation (ENSO), deep ocean heat uptake, and so forth. A recent paper I like by Foster and Rahmsdorf (discussed here and here) takes a statistical approach to attempt to eliminate the effect of the other known forcing mechanisms, and what’s left over is a fairly steady warming. Others have noted, more casually, that 2011 was the warmest La Niña year on record.

I decided to take a simple approach at looking at the effect of ENSO. Using GISTemp Land/Ocean Index values andNiño 3.4 values, I computed 12-month running averages of Niño 3.4 and compared them to the average GISTemp values at lags of 0, 3, and 6 months. Foster and Rahmsdorf used a diferent ENSO index and found optimal lags between 2 and 5 months. So one would guess that a 3-month lag would fit the data best in my case, and indeed it did.

The normal threshold for El Niño or La Niña, as applied by the Climate Prediction Center, is for five consecutive months of at least 0.5 C above or below normal in a key region of the tropical Pacific. For working with annual data, I decided to call an annual average above 0.5 C an El Niño and an annual average below -0.5 C a La Niña. Then I plotted it up, color-coding each year for whether it was El Niño, La Niña, or neither (neutral). Here’s the result:

GISTemp global temperatures, 1951-2011

We see the latter half of the mid-century flat period, followed by the warming since 1970 and the relatively flat recent few years. We also see a few years that were exceptionally cold and whose timing fits with the known injection of aerosols into the stratosphere by the mighty volcanic eruptions of Agung and Pinatubo. It’s easy to see that both of these eruptions caused global temperatures to drop by about 0.3 C temporarily before recovering as the aerosols settled out of the stratosphere over the following 2-3 years. Finally, we see that, as is well known, La Niña years tend to be globally cold years and El Niño years tend to be globally warm, with a global lag of three months as mentioned earlier. And, we see that in a head-to-head match between El Niño and Pinatubo, Pinatubo wins.

To dig deeper, I’ll zoom in on the period since Agung. This isolates the period of nearly steady warming since 1970 and lets us focus a bit more on what has happened since 1998 or so. Here’s the chart:

GISTemp global temperatures from 1967 to present

Somehow, it no longer appears that global temperatures have leveled off in the past decade. That is because, with the color coding according to the phase of ENSO, the eye is able to compare apples to apples: the upward long-term trend during El Niño years (red triangles) is plain, the upward long-term trend during neutral years (green squares) is plain, and the upward long-term trend during La Niña years (blue diamonds) is plain.

Stare hard enough, though, and you see that they have leveled off. The last ten data points have little or no trend. But we see that the lack of trend is at least partly due to the El Niño year near the beginning of the 10-year period and the two La Niña years near the end.

Let’s get quantitative about this. In this case, with the temperature rise being nearly linear, it helps to add trendlines. I’ve excluded the three Pinatubo years from the regressions. Here’s the result:

GISTemp global temperatures, with trends for El Niño, neutral, and La Niña years computed separately. Pinatubo years are excluded.

There aren’t that many full-blown El Niño events, but they seem to be following a steady upward trend. There are more La Niña events, and they too clearly follow a steady upward trend. Finally, the many neutral years also so no sign of departing from a steady upward trend. There’s enough scatter in the neutral years that if one had considered the period 1977-1987, or the period 1987-1997, one might be tempted to say that the neutral years had little or no warming. But the past decade fits nicely with the long-term upward trend of 0.16 C/decade shown by all three time series.

The spacing between the lines is a good measure of the impact of El Niño and La Niña. All else being equal, an El Niño year will average about 0.2 C warmer globally than a La Niña year. Each new La Niña year will be about as warm as an El Niño year 13 years prior.

So we see a couple of recent La Niñas have caused the recent global temperature trend to level off. But be honest: doesn’t it seem likely that, barring another major volcanic eruption, the next El Niño will cause global temperatures to break their previous record? Doesn’t it appear that whatever has caused global temperatures to rise over the past four decades is still going strong?

So about that lack of warming: Yes, it’s real. You can thank La Niña.

As for whether this means that Tyndall gases are no longer having an impact: Nice try.

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Below, Gammon was prominently featured in my post on drought conditions in Texas.

55 Responses to “The “Lack of Recent Warming” Crock: No Cigar, But Thanks for Playing!”

NOAA: “Both historical and near-real-time GHCN data undergo rigorous quality assurance reviews. These reviews include preprocessing checks on source data, time series checks that identify spurious changes in the mean and variance, spatial comparisons that verify the accuracy of the climatological mean and the seasonal cycle, and neighbor checks that identify outliers from both a serial and a spatial perspective.”

Presumably nobody here has bothered to contact the NWS’s around the world, so the raw data are still out of reach. I surmise it will always be so, given even the BEST people had to make a lot of assumptions and simplifications (and AFAIK haven’t published anything on peer-review papers as yet?).

We should try to live with the underlying uncertainty rather than claiming absolute certainty or absolute uncertainty.

But when it’s demonstrated the raw data produces global-average results very similar to the adjusted data, then the data ceases to be, shall we say, “raw enough”.

And as for contacting the NWS’s around the world, the energy companies, Koch Brothers, etc. have plenty of money to undertake that task. A complete verification of the GHCN data vs data obtained directly from the NWS’s would require nothing more than a cell phone (to call the NWS offices), a laptop (to crunch the data) and a few man-weeks of labor.

If there were even the slightest possibility of discrepancies between the GHCN raw data and data available directly from the NWS offices, those discrepancies would have been uncovered *years* ago. But the deniers have *nothing* — so they have no choice but to resort to the ludicrous tinfoil-hattery displayed by “omnologos” (who at least had the good sense to crop the tinfoil-hat out of his picture) and others here.

Followup to add: The energy industry could easily fund such a study out of “petty cash” — in fact, the Koch Brothers could probably dig enough change out from under their sofa cushions (actually, they’d have their servants do that) to fund the study.

just an aside, thanks to all for this illuminating discussion.
Suyts, as the new troll on the block, I hope you’ll apologize for insinuating that I vet comments unfairly here.

As any of the regulars will tell you, this is an open forum with very little interference by me – I rely on my very adept and expert commenters to police the BS – so as long as you are civil, you don’t even have to be sane to post here. For instance, if Lord Monckton wants to post trial results from his AIDS cure, I’d love to see them.
Just 2 rules here, no threats, and keep it at least PG13.

Hey, Green, give me a break! I had a comment in moderation for quite some time…. what was I suppose to think? At any rate, I do appreciate the open forum. My apologies and thanks…… I guess…. but, I suppose the moniker of “troll” is in the eye of the beholder.

Thought I’d provide some info about the GHCN daily *raw* data format to show how absolutely *nothing* has been hidden or modified.

A quick look at the GHCN daily data format (thoroughly documented, BTW — Google is your friend here) will confirm that every single temperature reading (good *or* bad) is included in the daily data updates.

Bad readings (i.e. data points that fail quality-control tests) are not deleted or modified — they are marked with the appropriate error code. It is a simple matter to configure your program to parse out that error code and do with the “bad” temperature reading as you see fit (include it, exclude it, downweight it, whatever).

Absolutely nothing is hidden — it’s all out there for anyone to scrutinize.

So, those who don’t trust the monthly raw data are perfectly free to compute their own monthly average temperatures — all they have to do is download the *daily* raw data, write their own software to parse out the data and error flags, and then deal with the flagged samples however they wish when they compute their own monthly averages.

The monthly data updates are provided as a *convenience* — having monthly data saves people the trouble (and data-plan overage charges) of having to process all the daily data themselves. You can be certain that if the monthly data were not provided, deniers would be howling about the hassle and inconvenience of dealing with the large and unwieldy daily temperature data files (even though most of them wouldln’t have the slighest idea what to do with the data in the first place).

And a followup note re: Spencer and his “population density” adjustments.

This is just one more screwup to be added to a very long list of “climate skeptic” screwups. With respect to global-warming skepticism/denial, here is something that you can take to the bank. Any time a skeptic/denier claims that he/she has found some fatal flaw with some aspect of climate-science (whether it’s alleged UHI contamination, “dropped stations” issues, hockey-sticks generated from random noise, etc.) you can be almost absolutely certain that the individuals making the claim “stepped in it” big-time somewhere.

Some of the screwups are so obvious (like Spencer’s population density screwup) that amateurs can flag the errors — other screwups may require more advanced technical knowledge to recognize. But nonetheless, virtually every denier attack on any aspect of climate science that you can imagine is based on at least one (often embarrassing) screwup.

A followup re: Spencer — if he was trying to “back out” a temperature-trend to a “zero-population density” trend, then using zero-population-density as the baseline would be the thing to do.

However, there’s no physical basis for the claim that surface temperatures are extremely sensitive to population densities, especially when you consider that Spencer’s “population density corrections” give you an answer that completely conflicts with the mid-tropospheric temperature trends derived from satellite data.

The huge mismatch between the mid-tropospheric trends measured by satellites and the “population-density corrected” surface temperature trend has no possible physical basis. To reconcile the mismatch, Spencer would have to come up with a plausible physical process that would cause variations in rural population density to propagate through the entire atmospheric column. That just isn’t going to happen, unless basic laws of thermodynamics are suspended.